Metabolic Diseases and Risk of Head and Neck Cancer: A Cohort Study Analyzing Nationwide Population-Based Data.
Soo-Young ChoiHyeon-Kyoung CheongMin-Kyeong LeeJeong Wook KangYoung-Chan LeeIn-Hwan OhYoung Gyu EunPublished in: Cancers (2022)
The aim of the study was to investigate the association between metabolic diseases and the risk of head and neck cancer (HNC) using nationwide population-based big data. This retrospective cohort study was conducted using the Korean National Health Insurance Service health checkup database. A total of 4,575,818 participants aged >40 years who received a health checkup in 2008 were enrolled, and we studied the incidence of HNC until 2019. We analyzed the risk of HNC according to the presence of metabolic diseases, such as obesity, dyslipidemia, hypertension, and diabetes. Although metabolic syndrome itself was not associated with HNC, each component of metabolic syndrome was associated with HNC. Underweight and diabetes were risk factors for HNC (HR: 1.694). High total cholesterol and high low-density lipoprotein cholesterol levels were factors that decreased the risk (HR 0.910 and 0.839). When we analyzed men and women separately, low total cholesterol level, low low-density lipoprotein cholesterol level, and hypertension were risk factors only in men. In addition, pre-obesity, obesity, and central obesity decreased the risk only in men. Each metabolic disease affects HNC in different ways. Underweight and diabetes increased the risk of HNC, whereas high total cholesterol and high low-density lipoprotein cholesterol levels decreased the risk of HNC.
Keyphrases
- metabolic syndrome
- type diabetes
- insulin resistance
- big data
- health insurance
- risk factors
- weight loss
- healthcare
- cardiovascular disease
- public health
- high fat diet induced
- blood pressure
- glycemic control
- mental health
- weight gain
- machine learning
- cardiovascular risk factors
- artificial intelligence
- adipose tissue
- body mass index
- skeletal muscle
- quality improvement
- physical activity
- cross sectional
- deep learning
- electronic health record
- affordable care act
- solid state
- human health